Abstract-We present a formal framework that combines high-level representation and causality-based reasoning with low-level geometric reasoning and motion planning. The framework features bilateral interaction between task and motion planning, and embeds geometric reasoning in causal reasoning, thanks to several advantages inherited from its underlying components. In particular, our choice of using a causality-based high-level formalism for describing action domains allows us to represent ramifications and state/transition constraints, and embed in such formal domain descriptions externally defined functions implemented in some programming language (e.g., C++). Moreover, given such a domain description, the causal reasoner based on this formalism (i.e., the Causal Calculator) allows us to compute optimal solutions (e.g., shortest plans) for elaborate planning/prediction problems with temporal constraints. Utilizing these features of high-level representation and reasoning, we can combine causal reasoning, motion planning and geometric planning to find feasible kinematic solutions to task-level problems. In our framework, the causal reasoner guides the motion planner by finding an optimal task-plan; if there is no feasible kinematic solution for that task-plan then the motion planner guides the causal reasoner by modifying the planning problem with new temporal constraints. Furthermore, while computing a task-plan, the causal reasoner takes into account geometric models and kinematic relations by means of external predicates implemented for geometric reasoning (e.g., to check some collisions); in that sense the geometric reasoner guides the causal reasoner to find feasible kinematic solutions. We illustrate an application of this framework to robotic manipulation, with two pantograph robots on a complex assembly task that requires concurrent execution of actions. A short video of this application accompanies the paper.

A nswer set programming (ASP) is a knowledge representation and reasoning (KR) paradigm. It has rich highlevel representation languages that allow recursive definitions, aggregates, weight constraints, optimization statements, default negation, and external atoms. With such expressive languages, ASP can be used to declaratively represent knowledge (for example, mathematical models of problems, behaviour of dynamic systems, beliefs and actions of agents) and solve combinatorial search problems (for example, planning, diagnosis, phylogeny reconstruction) and knowledge-intensive problems (for example, query answering, explanation generation). The idea is to represent a problem as a "program" whose models (called "answer sets" Lifschitz 1988, 1991]

Extended-spectrum-beta-lactamase (ESBL)-producing Escherichia coli (ESBL E. coli) strains are of major concern because few antibiotics remain active against these bacteria. We investigated the association between the fecal relative abundance (RA) of ESBL-producing E. coli (ESBL-RA) and the occurrence of ESBL E. coli urinary tract infections (UTIs). The first stool samples passed after suspicion of UTI from 310 women with subsequently confirmed E. coli UTIs were sampled and tested for ESBL-RA by culture on selective agar. Predictive values of ESBL-RA for ESBL E. coli UTI were analyzed for women who were not exposed to antibiotics when the stool was passed. ESBL E. coli isolates were characterized for ESBL type, phylogroup, relatedness, and virulence factors. The prevalence of ESBL E. coli fecal carriage was 20.3%, with ESBL E. coli UTIs being present in 12.3% of the women. The mean ESBL-RA (95% confidence interval [CI]) was 13-fold higher in women exposed to antibiotics at the time of sampling than in those not exposed (14.3% [range, 5.6% to 36.9%] versus 1.1% [range, 0.32% to 3.6%], respectively; P < 0.001) and 18-fold higher in women with ESBL E. coli UTI than in those with another E. coli UTI (10.0% [range, 0.54% to 100%] versus 0.56% [range, 0.15% to 2.1%[, respectively; P < 0.05). An ESBL-RA of <0.1% was 100% predictive of a non-ESBL E. coli UTI. ESBL type, phylogroup, relatedness, and virulence factors were not found to be associated with ESBL-RA. In conclusion, ESBL-RA was linked to the occurrence of ESBL E. coli UTI in women who were not exposed to antibiotics and who had the same clone of E. coli in urine samples and fecal samples. Especially, a low ESBL-RA appeared to be associated with a low risk of ESBL E. coli infection.

This note is about the relationship between two theories of negation as failure-one based on program completion, the other based on stable models, or answer sets. François Fages showed that if a logic program satisfies a certain syntactic condition, which is now called "tightness," then its stable models can be characterized as the models of its completion. We extend the definition of tightness and Fages' theorem to programs with nested expressions in the bodies of rules, and study tight logic programs containing the definition of the transitive closure of a predicate.2 The double negation in the first rule of (1) is redundant from the point of view of the completion semantics, but it does affect the program's answer sets. On the other hand, the second rule is redundant from the point of view of the answer set semantics. But, generally, dropping a rule like this can change a program's completion in an essential way. 3

We describe the reconstruction of a phylogeny for a set of taxa, with a character-based cladistics approach, in a declarative knowledge representation formalism, and show how to use computational methods of answer set programming to generate conjectures about the evolution of the given taxa. We have applied this computational method in two domains: historical analysis of languages and historical analysis of parasite-host systems. In particular, using this method, we have computed some plausible phylogenies for Chinese dialects, for Indo-European language groups, and for Alcataenia species. Some of these plausible phylogenies are different from the ones computed by other software. Using this method, we can easily describe domain-specific information (e.g., temporal and geographical constraints), and thus prevent the reconstruction of some phylogenies that are not plausible.